Mapping the Intellectual Structure of Social Entrepreneurship Research: A Citation/Co-citation Analysis

In this paper, we employ bibliometric analysis to empirically analyse the research on social entrepreneurship published between 1996 and 2017. By employing methods of citation analysis, document co-citation analysis, and social network analysis, we analyse 1296 papers containing 74,237 cited references and uncover the structure, or intellectual base, of research on social entrepreneurship. We identify nine distinct clusters of social entrepreneurship research that depict the intellectual structure of the field. The results provide an overall perspective of the social entrepreneurship field, identifying its influential works and analysing scholarly communication between these works. The results further aid in clarifying the overall centrality features of the social entrepreneurship research network. We also examine the integration of ethics into social entrepreneurship literature. We conclude with a discussion on the structure and evolution of the social entrepreneurship field

ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing

In this paper, we present a low-power anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves low-power operation through a combination of (a) careful choice of algorithm for online learning and (b) approximate computing techniques to lower average energy. In particular, online pseudoinverse update method (OPIUM) is used to train a randomized neural network for quick and resource efficient learning. An additional 42% energy saving can be achieved when a lighter version of OPIUM method is used for training with the same number of data samples lead to no significant compromise on the quality of inference. Instead of a single classifier with large number of neurons, an ensemble of K base learner approach is chosen to reduce learning memory by a factor of K. This also enables approximate computing by dynamically varying the neural network size based on anomaly detection. Fabricated in 65nm CMOS, the ADIC has K = 7 Base Learners (BL) with 32 neurons in each BL and dissipates 11.87pJ/OP and 3.35pJ/OP during learning and inference respectively at Vdd = 0.75V when all 7 BLs are enabled. Further, evaluated on the NASA bearing dataset, approximately 80% of the chip can be shut down for 99% of the lifetime leading to an energy efficiency of 0.48pJ/OP, an 18.5 times reduction over full-precision computing running at Vdd = 1.2V throughout the lifetime.Comment: 1

EBBINNOT: A Hardware Efficient Hybrid Event-Frame Tracker for Stationary Dynamic Vision Sensors

As an alternative sensing paradigm, dynamic vision sensors (DVS) have been recently explored to tackle scenarios where conventional sensors result in high data rate and processing time. This paper presents a hybrid event-frame approach for detecting and tracking objects recorded by a stationary neuromorphic sensor, thereby exploiting the sparse DVS output in a low-power setting for traffic monitoring. Specifically, we propose a hardware efficient processing pipeline that optimizes memory and computational needs that enable long-term battery powered usage for IoT applications. To exploit the background removal property of a static DVS, we propose an event-based binary image creation that signals presence or absence of events in a frame duration. This reduces memory requirement and enables usage of simple algorithms like median filtering and connected component labeling for denoise and region proposal respectively. To overcome the fragmentation issue, a YOLO inspired neural network based detector and classifier to merge fragmented region proposals has been proposed. Finally, a new overlap based tracker was implemented, exploiting overlap between detections and tracks is proposed with heuristics to overcome occlusion. The proposed pipeline is evaluated with more than 5 hours of traffic recording spanning three different locations on two different neuromorphic sensors (DVS and CeleX) and demonstrate similar performance. Compared to existing event-based feature trackers, our method provides similar accuracy while needing approx 6 times less computes. To the best of our knowledge, this is the first time a stationary DVS based traffic monitoring solution is extensively compared to simultaneously recorded RGB frame-based methods while showing tremendous promise by outperforming state-of-the-art deep learning solutions.Comment: 16 pages, 13 figure

New host and geographical record of Eudactylina pusilla Cressey, 1967 from Indian waters with DNA barcodes

The present paper reports the first record of the parasite Eudactylina pusilla Cressey, 1967 from the gills of the pelagic thresher shark, Alopias pelagicus Nakamura, 1935 collected during a multifilament longline operation at a depth of 762 m from Indian EEZ around Andaman Islands. The occurrence of this copepod gill parasite on A. pelagicus in the Indian waters constitutes new host record and extends the parasite’s known geographical distribution, thus contributing to the knowledge of biodiversity of the parasitic copepods in Indian waters. Molecular marker based taxonomical annotation using Mitochondrial 18S r DNA sequencing also confirmed the identity of the E. pusilla specimen

Clinical outcome, viral response and safety profile of chloroquine in COVID-19 patients — initial experience

Introduction: Chloroquine and its analogues are currently being investigated for the treatment and post exposure prophylaxis of COVID-19 due to its antiviral activity and immunomodulatory activity.Material and methods: Confirmed symptomatic cases of COVID-19 were included in the study. Patients were supposed to receive chloroquine (CQ) 500 mg twice daily for 7 days. Due to a change in institutional protocol, initial patients received chloroquine and subsequent patients who did not receive chloroquine served as negative controls. Clinical effectiveness was determined in terms of timing of symptom resolution and conversion rate of reverse transcriptase polymerase chain reaction (RT-PCR) on day 14 and day 15 of admission.Results: Twelve COVID-19 patients formed the treatment arm and 17 patients were included in the control arm. The duration of symptoms among the CQ treated group (6.3 ± 2.7 days) was significantly (p-value = 0.009) lower than that of the control group (8.9 ± 2.2 days). There was no significant difference in the rate of RT-PCR negativity in both groups. 2 patients out of 12 developed diarrhea in the CQ therapy arm.  Conclusion: The duration of symptoms among the treated group (with chloroquine) was significantly lower than that of the control group. RT-PCR conversion was not significantly different between the 2 groups

Extent of knowledge and attitudes on plagiarism among undergraduate medical students in South India - a multicentre, cross-sectional study to determine the need for incorporating research ethics in medical undergraduate curriculum

BACKGROUND: Undergraduate medical students in India participate in various research activities However, plagiarism is rampant, and we hypothesize that it is the lack of knowledge on how to avoid plagiarism. This study’s objective was to measure the extent of knowledge and attitudes towards plagiarism among undergraduate medical students in India. METHODS: It was a multicentre, cross-sectional study conducted over a two-year period (January 2018 – December 2019). Undergraduate medical students were given a pre-tested semi-structured questionnaire which contained: (a) Demographic details; (b) A quiz developed by Indiana University, USA to assess knowledge; and (c) Attitudes towards Plagiarism (ATP) questionnaire. RESULTS: Eleven medical colleges (n = 4 government medical colleges [GMCs] and n = 7 private medical colleges [PMCs]) participated. A total of N = 4183 students consented. The mean (SD) knowledge score was 4.54 (1.78) out of 10. The factors (adjusted odds ratio [aOR]; 95% Confidence interval [CI]; p value) that emerged as significant predictors of poor knowledge score were early years of medical education (0.110; 0.063, 0.156; < 0.001) and being enrolled in a GMC (0.348; 0.233, 0.463; < 0.001).The overall mean (SD) scores of the three attitude components namely permissive, critical and submissive norms were 37.56 (5.25), 20.35 (4.20) and 31.20 (4.28) respectively, corresponding to the moderate category. CONCLUSION: The overall knowledge score was poor. A vast majority of study participants fell in the moderate category of attitude score. These findings warrant the need for incorporating formal training in the medical education curriculum

Live demonstration : autoencoder-based predictive maintenance for IoT

This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from sensors attached to a motor. Faults can be triggered artificially in real-time to demonstrate anomaly detection.NRF (Natl Research Foundation, S’pore)Accepted versio

A stacked autoencoder neural network based automated feature extraction method for anomaly detection in on-line condition monitoring

Condition monitoring is one of the routine tasks in all major process industries. The mechanical parts such as a motor, gear, bearing are the major components of a process industry and any fault in them may cause a total shutdown of the whole process, which may result in serious losses. Therefore it is very crucial to predict any approaching defects before its occurrence. Several methods exist for this purpose and many research are being carried out for better and efficient models. However, most of them are based on the processing of raw sensor signals, which is tedious and expensive. Recently, there has been an increase in the feature based condition monitoring, where only the useful features are extracted from the raw signals and interpreted for the prediction of the fault. Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data. This of course requires the prior knowledge of the nature of data and related processes. This limits the feature extraction process. However, recent development in the autoencoder based feature extraction method provides an alternative to the traditional handcrafted approaches; however, they have mostly been confined in the area of image and audio processing. In this work, we have developed an automated feature extraction method for on-line condition monitoring based on the stack of the traditional autoencoder and an on-line sequential extreme learning machine (OSELM) network. The performance of this method is comparable to that of the traditional feature extraction approaches. The method can achieve 100% detection accuracy for determining the bearing health states of NASA bearing dataset. The simple design of this method is promising for the easy hardware implementation of Internet of Things (IoT) based prognostics solutions.NRF (Natl Research Foundation, S’pore)Accepted versio

Conditional acceptance of digitized business model innovation at the BoP: A stakeholder analysis of eKutir in India

The current research explores the contingencies behind the acceptance or rejection of digitized business model innovation at the Bottom of the Pyramid (BoP). Building on the existing literature on business model innovation and using the lens of stakeholder theory, we explore the contingencies that decide the success or failure of digitized business models at the BoP. We conducted an inductive case study of eKutir, an Indian social enterprise that uses a digital platform to deliver value for farmers in Orissa. Our analysis reveals that stakeholder's stability and stakeholder's incentives are the critical contingencies deciding the conditional acceptance of the digitized business model innovation. Our results also confirm that accessibility, availability, affordability, awareness and acceptability are the most important factors contributing to the stakeholder's adoption of digitized business model innovation. Further, we infer that age, respect, power and authority are key differentiating factors contributing to stakeholder's stability which can significantly influence the acceptance of digitized business model innovation. We conclude with a framework that can guide the successful implementation of digitized business model innovation at the BoP